Detection and Analysis of Depression Level Using Social Media

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https://doi.org/10.22214/ijraset.2021.37359

August 2021

International Journal for Research in Applied Science & Engineering Technology (IJRASET) ISSN: 2321-9653; IC Value: 45.98; SJ Impact Factor: 7.429 Volume 9 Issue VIII Aug 2021- Available at www.ijraset.com

Detection and Analysis of Depression Level Using Social Media Pragati J. Dhengale1, Nikita G. Zade2, Sakshi S Hedau3, Pooja R. Sharma4, Pallavi P. Lanjewar 5, Prof. Y. D. Choudhary6 1, 2, 3, 4, 5, 6

Department Of information technology, Karmaveer Dadasaheb Kannamwar College of Engineering, Nagpur

Abstract: Detection of depression through messages sent by a user on social media are often a fancy task because of the recognition and trends in them. In recent years, messages and social media has over up being a really shut illustration of a person’s life and his status. This is often an enormous stockpile of information a couple of person’s behaviour and might be used for detection of varied mental sicknesses (depression in our case) victimisation tongue process. This project is regarding constructing a model victimisation NLP to predict such mental disorders. Short-term memory networks square measure wellsuited to classifying, process and creating predictions supported statistic knowledge, since there are often lags of unknown length between necessary events during a statistic.

Keywords: Depression, social media, mental illness, NLTK I. INTRODUCTION Depression as a typical psychological state disorder has long been denned as one illness with a group of diagnostic criteria. It usually co-occurs with anxiety or alternative psychological and physical disorders; and has a bearing on feelings and behaviour of the affected people. In keeping with the World Health Organization study, there are 322 million folks calculable to suffer from depression, cherish four.4% of the worldwide population. In today’s world, communication through social media is rising as an enormous deal. They’re willing to share their thoughts, stories and their personal feelings, mental states, needs on social network sites, blogging platforms etc... Receivers use the manuscripts from emails and alternative varieties of social media comments to make correct reasoning and to correct the mistakes. Once folks write digitally on social media, their texts are processed mechanically. Linguistic communication process techniques are accustomed infer people’s mental behaviour.

According to World Health Organization, depression could be a common worldwide folio that affects a colossal quantity of people no matter their age. There are multiple factors that interfere period of time detection and treatment like lack of skilled specialists, social shaming, improper identification so on. The ever-lasting depression disorder could lead on to suicide if the depressed people don't seem to be equipped correct consultance, instant facilitate and might additionally suffer from anxiety. This work is targeted on the detection of depression and anxiety from tweets. The experiment conducted throughout this work needs the text knowledge that the chosen knowledge supply is Twitter wherever folks tweet regarding their feelings, hopes, desires, thoughts, stories and mental states. The goals of our analysis are: collect the in public on the market media messages of healthy and self-diagnosed people that contains mixed emotions therefore measure the extracted Twitter knowledge and apply NLTK and text blob to predict depressive and anxiety tweets. We are able to hunt for an answer to a performance increase through a correct options choice and their multiple feature mixtures. First, we decide the foremost helpful linguistic options applied for depression identification to characterize the content of the posts. Second, we have a tendency to analyze the correlation significance, hidden topics and word frequency extracted from the text. We have a tendency to compare the performance results supported 3 single feature sets and their multiple feature mixtures. In our experiment, we have a tendency to use knowledge collected from the Reddit social media platform.

II. AIMS AND OBJECTIVE A. Identify the foremost effective deep neural spec among some of elect architectures that were with success utilized in tongue process tasks.

B. The architectures square measure wont to notice users with signs of mental diseases (depression in our case) given restricted unstructured text knowledge extracted from the Twitter social media platform.

C. To investigate the result of depression detection, we have a tendency to proposed technique as associate economical and ascendable technique.

D. The main contribution of this study lies in exploiting a chic, diverse, and discriminating feature set that contains each tweet text and behavioural trends of various users.

E. This study are often extended within the future by considering additional deciliter models that live} extremely unlikely to overfit the used knowledge and notice a additional dependable thanks to measure the features’ impact.

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International Journal for Research in Applied Science & Engineering Technology (IJRASET) ISSN: 2321-9653; IC Value: 45.98; SJ Impact Factor: 7.429 Volume 9 Issue VIII Aug 2021- Available at www.ijraset.com III. LITERATURE SURVEY Michael M. Tadesse, Hongfei Lin, Bo Xu, and Liang Yang: we can significantly improve performance accuracy. The best single feature is bigram with the Support Vector Machine (SVM) classifier to detect depression with 80% accuracy and 0.80 F1 scores.

The strength hand effectiveness of the combined features (LIWC+LDA+bigram) are most successfully demonstrated with the Multilayer Perceptron (MLP) classifier resulting in the top performance for depression detection reaching 91% accuracy and 0.93 F1 scores.

P.V. Rajaraman, AsimNath, Akshaya.P.R, ChaturBhuja.G: Messages and social media has ended up being a very close representation of a person’s life and his mental state. This is a huge stockpile of data about a person’s behaviour and can be used for detection of various mental illnesses (depression in our case) using Natural Language Processing.

Akshi Kumara, Aditi Sharmab, Anshika Arorac: - This mixed anxiety-depressive disorder is a predominantly associated with erratic thought process, restlessness and sleeplessness. Based on the linguistic cues and user posting patterns, the feature set is defined using a 5-tuple vector <word, timing, frequency, sentiment, contrast>. An anxiety-related lexicon is built to detect the presence of anxiety indicators. Time and frequency of tweet is analyzed for irregularities and opinion polarity analytics is done to find inconsistencies in posting behaviour. The model is trained using three classifiers (multinomial naïve bayes, gradient boosting, and random forest) and majority voting using an ensemble voting classifier is done.

Michael M. Tadesse , Hongfei Lin , Bo Xu , Liang Yang: We can significantly improve performance accuracy. The best single feature is bigram with the Support Vector Machine (SVM) classifier to detect depression with 80% accuracy and 0.80 F1 scores.

The strength and effectiveness of the combined features (LIWC+LDA+bigram) are most successfully demonstrated with the Multilayer Perceptron (MLP) classifier resulting in the top performance for depression detection reaching 91% accuracy and 0.93 F1 scores. According to our study, better performance improvement can be achieved by proper feature selections and their multiple feature combinations.

HoyunSong, Jinseon You, and Jin-Woo Chung Jong C. Park: we propose Feature Attention Network (FAN), inspired by the process of diagnosing depression by an expert who has background knowledge about depression. We evaluate the performance of our model on a large scale general forum (Reddit Self-reported Depression Diagnosis) dataset. Experimental results demonstrate that FAN shows good performance with high interpretability despite a smaller number of posts in training data. We investigate different aspects of posts by depressed users through four feature networks built upon psychological studies, which will help researchers to investigate social media posts to find useful evidence for depressive symptoms.

Raza Ul Mustafa, Noman Ashraf, Fahad Shabbir, Ahmed Javed Ferzund, Basit Shahzad, Alexander Gelbukh : A sample of their recent tweets collected ranges from (200 to 3200) tweets per person. From their tweets, we selected 100 most frequently used words using Term Frequency-Inverse Document Frequency (TF-IDF). Later, we used the 14 psychological attributes in Linguistic Inquiry and Word Count (LIWC) to classify these words into emotions. Moreover, weights were assigned to each word from happy to unhappy after classification by LIWC and trained machine learning classifiers to classify the users into three classes of depression High, Medium, and Low. According to our study, better features selections and their combination will help to improve performance and accuracy of classifiers.

Kali Cornn: a dataset of scraped Reddit comments, this project aims to classify depression in comments. Focusing on the setting of social media, this project explores methods of machine learning and neural network architectures for identifying depression in digitally shared text entries. This project developed machine learning (logistic regression, support vector machines), a BERT-based model, and neural networks with and without word embeddings (CNN) for this classification task.

IV. DESIGN AND IMPLEMENTATION A. Proposed System Mental illness detection in social media will be thought of a posh task, primarily because of the difficult nature of mental disorders.

In recent years, this analysis space has began to evolve with the continual increase in quality of social media platforms that became associate degree integral a part of people’s life. This shut relationship between social media platforms and their users has created these platforms to replicate the users’ personal life with completely different limitations. In such associate degree surroundings, researchers are conferred with a wealth of knowledge relating to one’s life. Additionally to the amount of quality in distinguishing mental sicknesses through social media platforms, adopting supervised machine learning approaches like deep neural networks haven't been wide accepted because of the difficulties in getting decent amounts of annotated coaching information. Because of these reasons, we tend to attempt to establish the foremost effective deep neural spec among a number of of hand-picked architectures that were with success utilized in tongue process tasks.

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International Journal for Research in Applied Science & Engineering Technology (IJRASET) ISSN: 2321-9653; IC Value: 45.98; SJ Impact Factor: 7.429 Volume 9 Issue VIII Aug 2021- Available at www.ijraset.com We tend to gift a brand new model NLTK with textblob. The chosen architectures ar accustomed observe users with signs of mental sicknesses (depression in our case) given restricted unstructured text information extracted from the Twitter social media platform.

Fig. 1 System flow Diagram

Fig. 2 Data Flow Diagram Data for the model are extracted from the Twitter web site. Information are in unstructured format and it'll have uncountable useless information. Thus we'll initial do information improvement. Once we have a tendency to clean the info we'll apply EDA for locating the polarity of the info like positive, neutral, or negative mechanically or several complicated sentiments like happiness, sadness, anger, joy, etc. Once EDA is finished can pass the info to the NLTK model that we can choose the model giving the simplest accuracy.

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International Journal for Research in Applied Science & Engineering Technology (IJRASET) ISSN: 2321-9653; IC Value: 45.98; SJ Impact Factor: 7.429 Volume 9 Issue VIII Aug 2021- Available at www.ijraset.com B. Modules 1) Information Selection: A coaching set is employed to coach the machine learning method to know the potential relationship between the informative variables and target variable.

2) Information Cleaning: information improvement suggests that filtering and modifying your information specified it's easier to explore, understand, and model. Filtering out the elements you do not wish or would like in order that you ought not to consider or method them.

3) Information imputation: Machine learning algorithms need numeric input values, and a worth to be gift for every row and column in a very dataset. As such, it's common to spot missing prices in a very dataset and replace them with a numeric value.

4) Information Analysis: a way of knowledge analysis that automates analytical model building. It’s a branch of computer science supported the concept that systems will learn from information, determine patterns and create selections with least human intervention.

5) Information image: information visualization is that the illustration of {information} or information in a very graph, chart, or alternative visual format. Machine learning makes it easier to conduct analyses like prophetic analysis, which might then function useful visualizations to gift.

6) Coaching: Training information is that the information you employ to coach associate degree algorithmic rule or machine learning model to predict the result you style your model to predict.

7) Testing: A take a look at dataset may be a dataset that's freelance of the coaching dataset, however that follows identical likelihood distribution because the coaching dataset.

8) Algorithmic rule selection: Machine learning algorithms ar the engines of machine learning, which means it's the algorithms that flip an information set into a model.

C. Planned Methodology 1) Linguistic communication Toolkit (NLTK): The linguistic communication Toolkit, or a lot of usually NLTK, may be a suite of libraries and programs for symbolic and applied mathematics linguistic communication process (NLP) for English written within the Python programing language. it had been developed by Steven Bird and Edward Loper within the Department of pc and data Science at the University of Pennsylvania. NLTK and text blob includes graphical demonstrations and sample information. It’s in the middle of a book that explains the underlying ideas behind the language process tasks supported by the toolkit, and a reference work. NLTK is meant to support analysis and teaching in information science or closely connected areas, together with empirical linguistics, scientific discipline, computer science, info retrieval, and machine learning. NLTK has been used with success as a teaching tool, as a private study tool, and as a platform for prototyping and building analysis systems. NLTK and text blob may be a powerful Python package that gives a collection of numerous natural languages algorithms. It is free, open supply, straightforward to use, massive community, and well documented. NLTK and text blob consists of the foremost common algorithms like tokenizing, part-of-speech tagging, stemming, sentiment analysis, topic segmentation, and named entity recognition. NLTK helps the pc to analysis, pre-process, and perceive the written communication.

V. RESULT AND DISCUSSION

Fig 3.Collection of data from twitter

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International Journal for Research in Applied Science & Engineering Technology (IJRASET) ISSN: 2321-9653; IC Value: 45.98; SJ Impact Factor: 7.429 Volume 9 Issue VIII Aug 2021- Available at www.ijraset.com Here, we firstly collect all the data from the tweets that user has been tweeted. This data includes name, time, data, emoji’s, stock words, etc.

Fig 4.Data cleaning and create word cloud We apply data cleaning on that collected data and create word cloud as shown in figure 4.

Fig 5.Grouping of positive tweets After collecting all the data we do separation of positive tweets as shown in figure 4.

Fig 6. Grouping of negative tweets

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International Journal for Research in Applied Science & Engineering Technology (IJRASET) ISSN: 2321-9653; IC Value: 45.98; SJ Impact Factor: 7.429 Volume 9 Issue VIII Aug 2021- Available at www.ijraset.com Similarly, we also do negative tweets separation as shown in figure 5.

Fig 5.chart Depending on the number of negative, positive and neutral polarities we create chart.

Fig 6.Result Our module detect the level of mental health depending on the word polarities that is positive, negative and neutral which has higher amount.

VI. CONCLUSION We have exhibited the capability of using twitter as a tool for measuring and detecting major depression among its users. To give a clear understanding of our work, numbers of research challenges were stated. The algorithms are designed to analyze the tweet for emotion detection as well as for detection of suicidal thoughts among people on social media. The mechanism does analysis of the tweets for prediction of depression without checking the validity of tweets. Social media is an open platform where many people refrain from telling their true emotions that might relate to depression they are facing, and so the model analysis here are mostly based on the prediction from posts using various machine learning algorithms. The main requirement of model is to be perfectly able to predict the result as there are a number of implementations that require verification of data before predicting the thoughts or posts of the person as suicidal or non-suicidal we are developed new model with combination NLTK & text blob for better Performance result.

REFERENCES [1] [2] [3] [4] [5] [6]

P.V. Rajaraman , AsimNath ,Akshaya.P.R , ChaturBhuja.G Depression Detection of Tweets and A Comparative Test, International Journal of Engineering Research & Technology (IJERT) ,Vol. 9 Issue 03, March-2020 Akshi Kumara, AditiSharmab, AnshikaArorac, Anxious Depression Prediction in Real-time Social Data, ICAESMT19, International Conference on Advanced Engineering, Science, Management and Technology – 2019 Michael M. Tadesse , Hongfei Lin , Bo Xu , And Liang Yang,Detection of Depression-Related Posts in Reddit Social Media Forum, Digital Object Identifier 10.1109/ACCESS.2019.2909180, April 4, 2019 Raza Ul Mustafa,Noman Ashraf,Fahad Shabbir Ahmed,Javed Ferzund,Basit, Shahzad,Alexander Gelbukh, A Multiclass Depression Detection in Social Media Based on Sentiment Analysis, 17th International Conference on Information Technology–New Generations (ITNG 2020) , 12 May 2020 Kali Cornn, Identifying Depression on Social Media, Department of Statistics Stanford University Stanford, CA 94305 Michael MesfinTadesse ,ongfei Lin ,Bo Xu, and Liang Yang ,Detection of Suicide Ideation in Social Media Forums Using ,State Key Laboratory of Cognitive Intelligence, Algorithms 2020, 13(1), 7,24 December 2019

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